Self-Organising Maps (SOM) are methods for unsupervised learning, based on a grid of artificial neurons whose weights are adapted to match input vectors in a training set. It was first described by the Finnish professor Teuvo Kohonen and is thus sometimes referred to as a Kohonen map. SOM is one of the most popular neural computation methods in use, and several thousand scientific articles have been written about it. SOM is especially good at producing visualizations of high-dimensional data.

Kohonen map used as space filters

Considering an image, a Kohonen map can be used to find most significant filters of a given size able to represent the image. The idea is consider an input vectors being sub-images of a given size (5x5 pixels in the example below) and to feed the network with these data. Once learning is done, it is possible to use these filters to reconstruct the whole image as shown below.

Kohonen map used as color filters

Another use of a Kohonen map onto an image is to use it to find statistically most significant colors of the image. This time, an input vector is a colored pixel from the image (3 dimensions: red, green & blue). Once learning is done, it is possible to use found prototypes to get a fair reconstruction of the image as shown below.